In these days, an information communication network systems such as a cellular phone and internet have advanced with increasing speed in respect of complexity and scale. In accordance with the advancement, an operator or surveillant engaged in the operation businesses or monitoring business of the information communication network system have the difficulty in accurately grasping the operational state on the system.
For example, one example of the information communication network systems includes a system typified by the cellular phone. In the system, there is a case where the observation data regarding the operational and radio environmental states of a base station and the cellular phone is observed in unit of hundred of items in the base station. Moreover, the number of base stations sometimes reaches tens of thousands. Accordingly, the amount of observation data to be collected increases to an extraordinary degree, and it is difficult for the operator or the surveillant to accurately grasp the operational state of the information communication network system.
In particular, the operation or monitoring business of the information communication network system include individual characteristic in the system (or unique to the system) and an element (or factor) that exerts influence on the characteristic. As a result, the operator or surveillant depends on individual skill such as associated knowledge and experience in the operation or monitoring businesses. Accordingly, it is a problem in the management or monitoring businesses to be qualitatively maintained and improved in the information communication network system.
In order to compensate for a lack of knowledge or experience for the manager or surveillant, for example, there is a case where a data mining technology is applied. The data mining technology, for example, is a technology that data analyzing technique such as artificial intelligence and so on is applied to a large amount of data, and knowledge is taken out.
However, regarding the data mining technique, for example, the processing time increases in accordance with the expansion or complication of the system, when the information communication network system is expanded or complicated. Accordingly, there is a limit in the case where the data mining technique is applied to the information communication network system.
Under the circumstances, for example, there is a technology as follows. That is, there is the technology that candidate for failure part based on each alarm signal is estimated, the failure part is identified automatically by finding a common set between the candidates for failure parts, and a part where failure occurs is diagnosed promptly by analyzing the factor of occurrence of the failure to be identified in accordance with the instructions by the operator.
Further, there is a causality relation derivation system to analyze the causality relation of data in appropriate memory capacity and processing time, by collecting data regarding yield of a product and calculating the causality relation of a multitude of data with a conditional probability, in a causality relation derivation kernel interface.    Japanese Laid-open Patent Publication No. 05-114899    Japanese Laid-open Patent Publication No. 2000-288877
However, the technology of estimating the candidates for failure part described above and the technology of calculating the causality relation with the conditional probability are aimed at estimating the failure part or calculating the causality relation of multitude of data. Accordingly, the technologies described above, for example, make it possible to estimate the failure part, but fail to notify the operator or surveillant what cause of the occurrence of failure is. Further, the technologies described above make it possible to calculate the causality relation of data, but fail to notify the operator or the surveillant what the cause hiding behind the data is.
Accordingly, in the information communication network system, the technologies described above fail to notify the operator or the surveillant what the cause hiding behind the observation data is, and what way to cope with the cause is, in a huge mass of the observation data.
For example, there is a case where fluctuation in traffic is monitored by the operator or the surveillant in the information communication network system typified by the cellular phone. The fluctuation in traffic has a characteristic of being substantially affected by transfer of the cellular phone and regional property (for example, residential area and business area), for example. Accordingly, it is difficult to instantly identify the cause such as the transfer of cellular phone, the regional property, and so on, which are attributed to the acquisition of the observation data, from the observation data collected by the base station regarding the fluctuations in traffic.
Or, it is difficult to instantly determine whether the cause of the deterioration is ascribed to the process ability of a system apparatus (for example, base station control apparatus) or a lack of radio resource and appropriately respond in the information communication network system, when value of the observation data is transferred in direction of deterioration with respect to a threshold value.
For example, in any one of the two technologies described above, it fails to identify cause of acquisition of the observation date based on the observation date.
Further, in the technologies described above, for example, there is no argument as to how appropriate observation data is identified or narrowed when the huge mass of observation data is obtained in the information communication network system. When the cause is identified from the huge mass of observation data in the information communication network system, there is a case that it takes a lot of time to identify the cause.